The Theory of Anything podcast

The Theory of Anything

Bruce Nielson

A podcast with episodes loosely tied together by Popper-Deutsch Theory of Knowledge. David Deutsch's 4 Strands ties everything together, so we discuss everything we find interesting be it science, philosophy, computation, politics, or art. Support this podcast:

36 avsnitt

  • The Theory of Anything podcast

    Episode 35: Physics and Relationalism: An Interview with Julian Barbour


    Sadia, in her four episodes on unsolved problems in physics (first episode here), was clearly heavily inspired by the work of Julian Barbour. So we invited Julian to join us for an episode and got a chance to ask him questions about his theories. Julian is a world-renowned physicist and author of several books on physics including The Janus Point, The End of Time, and The Discovery of Dynamics.  His theories include a challenge to the prevailing theory of entropy (i.e. heat death) and even hint as possible apparent teleology in cosmology (in this case a tendency towards novelty and variety.) We are very excited to have him on the show and to answer our questions about his theories.  --- Support this podcast:
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    Episode 34: Alpha Go and Creativity


    When Alpha Go beat Lee Sedol, the world Go champion, it came up with creative new moves never previously seen before and even invented a whole new style of play unknown to humans. IBM's Deep Blue, the champion chess algorithm, failed to do either of these. What was the difference? In this podcast, we review Alpha Go the Movie. Warning: Spoilers abound! Please go watch the movie first! This is an excellent movie.  Bruce (using his admittedly thin knowledge of reinforcement learning) explains how Alpha Go works (using the materials previously discussed in our Reinforcement Learning episode) and how Alpha Go came up with a creative new approach to Go that went beyond the knowledge of the programmers.  While Alpha Go definitely does not have "creativity" in the universal explainer sense of the word (it has no explanatory knowledge nor understanding), it did come up with a creative new playstyle never before seen in the history of the world that changed how humans play Go. Even the programmers were caught off guard by what it came up with. We talk about how Alpha Go challenges the Pseudo-Deutsch Theory of Knowledge but meshes well with Campbell's evolutionary epistemology.  --- Support this podcast:
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  • The Theory of Anything podcast

    Episode 33: Unsolved Problems in Physics Part 4 - Possible Solutions and Criticisms


    We wrap up our discussion with Sadia Naeem covering possible solutions and criticisms of those solutions.  --- Support this podcast:
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    Episode 32: Unsolved Problems in Physics Part 3 - Symmetry and Novelty


    Sadia Naeem continues the discussion about unsolved problems in physics. This time we talk about (among many other things) symmetry and novelty.  --- Support this podcast:
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    Episode 31: Unsolved Problems in Physics Part 2 - Clocks, Blocks, and Eternalism


    Sadia Naeem joins us again, this time to explain clocks, block universes, and eternalism.  --- Support this podcast:
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    Episode 30: Unsolved Problems in Physics Part 1 - The Mystery of Time


    Sadia Naeem joins us to discuss her own research and musings into the problems and mystery presented by time. --- Support this podcast:
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    Episode 29: The Marvel[ous] TV Shows


    In this episode Cameo, Tracy, and Bruce geek out over how good the Marvel TV shows are and how much they really get right. Spoilers abound, so be warned.  --- Support this podcast:
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    Episode 28: Reinforcement Learning and Q-Learning


    Reinforcement Learning is a machine learning algorithm that is a 'general purpose learner' (with certain important caveats). It generated a lot of excitement with its stunning victory of Alpha Go against Lee Sedol the world Go champion.    In this podcast, we go over the theory of reinforcement learning and how it works to solve any Markov Decision Problem (MDP).    This episode will be particularly useful for Georgia Tech OMSCS students taking classes that deal with Reinforcement Learning (ML4T, ML, RL) as we briefly explain the mathematics of how it works and show some simple examples.   This episode is best when watched on the Youtube channel, though we'll release an audio version as well. But the visuals are helpful here. The audio version is abbreviated and removes the mathematical theory and proof. --- Support this podcast:
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    Episode 27: Chiara Marletto and Constructor Theory


    In this episode, we interview Chiara Marletto about her recent book The Science of Can and Can't: A Physicist's Journey Through the Land of Counterfactuals as well as discussing Constructor Theory in general and how it might help us form a new mode of explanation in physics. We ask her some tough questions about constructor theory and she fields the questions very well.  For those interested in q-numbers vs real numbers, see Sam Kupyer's lecture on our Youtube channel. Follow us on Twitter. Check out our blog. --- Support this podcast:
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    Episode 26: Is Universal Darwinism the Sole Source of Knowledge Creation?


    Donald Campbell made the bold prediction that all expansions of knowledge will be found to require the Universal Darwinism algorithm of variation and selection. In this episode, we're going to test that prediction and see if it holds up against what we currently know about Artificial Intelligence and Machine Learning.    For example, does (apparent) knowledge created by Gradient Descent require variation and selection? Or is it really and truly inductive? Or does it just fail to create knowledge at all despite clearly creating improvements?    Ultimately, we'll find that Machine Learning creates an exciting set of epistemological problems that need to be solved! Youtube version with optional visuals --- Support this podcast:

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